Manipulation planning from high-level task specifications, even though highly desirable, is a challenging problem. The large dimensionality of manipulators and complexity of task specifications make the problem computationally intractable. This work introduces a manipulation planning framework with linear temporal logic (LTL) specifications. The use of LTL as the specification language allows the expression of rich and complex manipulation tasks. The framework deals with the state-explosion problem through a novel abstraction technique. Given a robotic system, a workspace consisting of obstacles, manipulable objects, and locations of interest, and a co-safe LTL specification over the objects and locations, the framework computes a motion plan to achieve the task through a synergistic multi-layered planning architecture. The power of the framework is demonstrated through case studies, in which the planner efficiently computes plans for complex tasks. The case studies also illustrate the ability of the framework in intelligently moving away objects that block desired executions without requiring backtracking.

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There are many applications where robots have to operate in environments that other agents can change. In such cases, it is desirable for the robot to achieve a given highlevel task despite interference. Ideally, the robot must decide its next action as it observes the changes in the world, i.e. act reactively. In this paper, we consider a reactive planning problem for finite robotic tasks with resource constraints. The task is represented using a temporal logic for finite behaviors and the robot must achieve the task using limited resources under all possible finite sequences of moves of other agents. We present a formulation for this problem and an approach based on quantitative games. The efficacy of the approach is demonstrated through a manipulation case study.

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The Covariant Hamiltonian Optimization and Motion Planning (CHOMP) algorithm has found many recent applications in robotics research, such as legged locomotion and mobile manipulation. Although integrating kinematic constraints into CHOMP has been investigated, prior work in this area has proven to be slow for trajectories with a large number of constraints. In this paper, we present Multigrid CHOMP with Local Smoothing, an algorithm which improves the runtime of CHOMP under constraints, without significantly reducing optimality. The effectiveness of this algorithm is demonstrated on two simulated problems, and on a physical HUBO+ humanoid robot, in the context of door opening. We demonstrate order-of-magnitude or higher speedups over the original constrained CHOMP algorithm, while achieving within 2% of the performance of the original algorithm on the underlying objective function.